Recent advances in medical imaging technology have introduced functional magnetic resonance imaging (fMRI) capable of acquiring sequences of images of brain activity by measuring changes in blood oxygenation levels. Functional magnetic resonance imaging is increasingly used in the medical field to scan subjects, both normal and diseased. The fMRI data is a 4-dimensional dataset involving 3 spatial dimensions and one temporal dimension. An fMRI dataset is very large and difficult to visualize for making meaningful conclusions. Typically, this data is processed by different analysis techniques to generate a number of “human viewable maps” which are then used to study the fMRI data and reach conclusions.
One of the most common approaches to processing fMRI data is known as the general linear model (GLM) technique, which makes use of an experimental protocol while a subject is being scanned. The GLM technique produces spatial maps of brain activity, which indicate those areas of the brain that are active for a given experiment (stimulus) being conducted on the target subject. More specifically, with the GLM technique, activity in different regions of while the subject is not conducting the given experimental task. A thresholding protocol or linear analysis is then performed on each dataset to determine if certain measured activity is beyond noise. Once spatial maps of brain activity are obtained for the measured activity with and without the experimental stimulus, the spatial maps are compared to determine which areas of the brain are differentially activated.
With the GLM protocol, the resulting spatial maps only provide information based on measured activity of different regions of the brain, independent of each other, and do not provide any information regarding how activity in one voxel relates to, or affects, or otherwise triggers, activity of another voxel. In other words, the spatial maps derived using the GLM technique do not provide any information about the dynamics of ongoing brain activity which is not directly related to the experimental task conducted. Therefore, it is not possible to fully summarize the fMRI data using such techniques and later use it for indexing, prediction, or classification purposes.